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About the database ScholarIQanswers from OpenAlex & ORCID
How has Aitor Álvarez-Gila's publication output changed over time?
ScholarIQpublication output · 2014–2020
Output grew0% over the shown period — from 1 works in 2014 to 1 in 2020.
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3
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1
20142017201820192020
What are the most-cited papers on Aitor Álvarez-Gila?
ScholarIQmost cited works
Automatic Red-Channel underwater image restoration
Adrián Galdrán, David Pardo, Artzai Picón, Aitor Álvarez-Gila
S105424869. 2014975 CitationsOPEN ACCESS
Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild
Artzai Picón, Aitor Álvarez-Gila, Maximiliam Seitz, Amaia Ortiz‐Barredo, Jone Echazarra, Alexander Johannes
Computers and Electronics in Agriculture. 2018558 Citations
Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case
Alexander Johannes, Artzai Picón, Aitor Álvarez-Gila, Jone Echazarra, Sergio Rodríguez-Vaamonde, Ana María Díez-Navajas, Amaia Ortiz‐Barredo
Computers and Electronics in Agriculture. 2017420 CitationsOPEN ACCESS
Few-Shot Learning approach for plant disease classification using images taken in the field
David Argüeso, Artzai Picón, Unai Irusta, Alfonso Medela, Miguel G. San-Emeterio, Arantza Bereciartua, Aitor Álvarez-Gila
Computers and Electronics in Agriculture. 2020365 Citations
Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions
Artzai Picón, Maximiliam Seitz, Aitor Álvarez-Gila, Patrick Mohnke, Amaia Ortiz‐Barredo, Jone Echazarra
Computers and Electronics in Agriculture. 2019220 CitationsOPEN ACCESS
Related on ScholarIQ
Euskadiko Parke Teknologikoa
Institution
Automatic Red-Channel underwater image restoration
Paper
Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild
Paper
Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case
Paper
Few-Shot Learning approach for plant disease classification using images taken in the field
Paper
Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions
Paper